Tracking articulated objects in image sequences remains a challenging problem, particularly in terms of the ability to localize the individual parts of an object given self-occlusions and changes in viewpoint. In this paper we propose a two-dimensional spatio-temporal modeling approach that handles both self-occlusions and changes in viewpoint. We use a Bayesian framework to combine pictorial structure spatial models with hidden Markov temporal-models. Inference for these combined models can be performed using dynamic programming and sampling methods. We demonstrate the approach for the problem of tracking a walking person, using silhouette data taken from a single camera viewpoint. Walking provides both strong spatial (kinematic) and temporal (dynamic) constraints, enabling the method to track limb positions in spite of simultaneous self-occlusion and viewpoint change.